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Recent Researches in Applied Computers and Computational Science Possibilistic Risk Aversion and Its Indicators 178 Irina Georgescu, Jani Kinnunen Statistical Analysis of Fade Events on FSO Systems 184 Zdenek Kolka, Viera Biolkova, Dalibor Biolek An Agent-Based Computational Model for Strategic Investment Decision Processes 188 G. Mircea, M. Pirtea, M. Neamtu, S. Băzăvan Teachers’ and Students’ Feedback Concerning the Use of ICT Tools in Learning Science through Nanotechnology Laura Monica Gorghiu, Gabriel Gorghiu 194 Closure Lattices for Avoiding Overtesting of Students 200 Sylvia Encheva Ranking Reusable Learning Objects with Rough Sets Based Methods 204 Sylvia Encheva Logical Implications 208 Sylvia Encheva Presenting a Combined E-Government Framework Using DSS and GIS 213 Shapoor Zarei, Amin Daneshmand Malayeri, Nikos E. Mastorakis E-Voting and E-Democracy: Basic Approach to E-Government Issuance 218 Shapoor Zarei, Amin Daneshmand Malayeri, Nikos E. Mastorakis A New Fast Neural Network Model 224 Hazem M. El-Bakry, Nikos Mastorakis Numerical Analysis of the Heat Transfer for Packing Design of Cryogenic Gate Valve 232 Si Pom Kim, Rock Won Jeon, Il Ju Hwang, Jae Hoon Lee, Won Heaop Shin Intelligent Model in B2C E-Commerce Using Fuzzy Approaches 238 Shapoor Zarei, Amin Daneshmand Malayeri, Nikos E. Mastorakis Automatic Creation of Countermeasure Plan against Process Delay: Creation of Countermeasures Based on Crashing and Fast-Tracking Yuichi Inoue, Kazuki Mori, Seiichi Komiya 243 Application of MATLAB–Based Solar Dryer for Cocoa Drying 252 Isaac Kuma Yeboah Authors Index 259 ISBN: 978-1-61804-084-8 11
Recent Researches in Applied Computers and Computational Science Plenary Lecture 1 On Robust Possibilistic C-Means Clustering Algorithm Professor Miin-Shen Yang Department of Applied Mathematics Chung Yuan Christian University Taiwan E-mail: msyang@math.cycu.edu.tw Abstract: Clustering is a method for finding clusters of a data set with the most similarity within the same cluster and the most dissimilarity between different clusters. It is a branch in multivariate statistical analysis and an unsupervised learning in pattern recognition. Since 1970, the fuzzy c-means (FCM) clustering algorithm has been well used in various applications. It is known that the robustness is important for clustering. However, the robustness for FCM is not enough. A first extension of FCM based on possibilistic c-partitions was the possibilistic c-means (PCM) clustering algorithm proposed by Krishnapuram and Keller in 1993. In this lecture, I will introduce a robust type of PCM. Since a merit of PCM is as a good mode-seeking algorithm if initials and parameters are suitably chosen, however, the performance of PCM heavily depends on initializations and parameters selection. In the robust PCM, we propose a mechanism of robust automatic merging. The proposed robust PCM algorithm first uses all data points as initial cluster centers and then automatically merges these surrounding points around each cluster mode such that it can self-organize data groups according to the original data structure. The robust PCM can exhibit the robustness to parameter, noise, cluster number, different volumes and initializations. Some numerical data and real data sets are used to show these good aspects of the robust PCM. Experimental results and comparisons actually demonstrate that the proposed robust PCM is an effective and parameter-free robust clustering algorithm. Brief Biography of the Speaker: Miin-Shen Yang received the BS degree in mathematics from the Chung Yuan Christian University, Chungli, Taiwan, in 1977, the MS degree in applied mathematics from the National Chiao-Tung University, Hsinchu, Taiwan, in 1980, and the PhD degree in statistics from the University of South Carolina, Columbia, USA, in 1989. In 1989, he joined the faculty of the Department of Mathematics in the Chung Yuan Christian University as an Associate Professor, where, since 1994, he has been a Professor. From 1997 to 1998, he was a Visiting Professor with the Department of Industrial Engineering, University of Washington, Seattle. During 2001-2005, he was the Chairman of the Department of Applied Mathematics in the Chung Yuan Christian University. His research interests include applications of statistics, fuzzy clustering, neural fuzzy systems, pattern recognition and machine learning. Dr. Yang is an Associate Editor of the IEEE Transactions on Fuzzy Systems, and an Associate Editor of the Applied Computational Intelligence and Soft Computing. He was awarded with 2008 Outstanding Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE; 2009 Outstanding Research Professor of Chung Yuan Christian University; 2010 Top Cited Article Award 2005-2010, Pattern Recognition Letters. ISBN: 978-1-61804-084-8 12
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